Instructions to use trillionlabs/TxGravity-30B-A5B-Open with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use trillionlabs/TxGravity-30B-A5B-Open with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="trillionlabs/TxGravity-30B-A5B-Open")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("trillionlabs/TxGravity-30B-A5B-Open")
model = AutoModelForCausalLM.from_pretrained("trillionlabs/TxGravity-30B-A5B-Open", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use trillionlabs/TxGravity-30B-A5B-Open with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "trillionlabs/TxGravity-30B-A5B-Open"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "trillionlabs/TxGravity-30B-A5B-Open",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/trillionlabs/TxGravity-30B-A5B-Open

SGLang

How to use trillionlabs/TxGravity-30B-A5B-Open with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "trillionlabs/TxGravity-30B-A5B-Open" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "trillionlabs/TxGravity-30B-A5B-Open",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "trillionlabs/TxGravity-30B-A5B-Open" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "trillionlabs/TxGravity-30B-A5B-Open",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use trillionlabs/TxGravity-30B-A5B-Open with Docker Model Runner:
```
docker model run hf.co/trillionlabs/TxGravity-30B-A5B-Open
```

sungjunhan-trl commited on Sep 9

Commit

802d85c

verified ·

1 Parent(s): 459a232

model card

Browse files

Files changed (1) hide show

README.md +80 -0

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	@@ -0,0 +1,80 @@

+---
+license: apache-2.0
+language:
+  - en
+library_name: transformers
+pipeline_tag: text-generation
+tags:
+  - moe
+  - mixture-of-experts
+  - gravity
+  - txgravity
+  - therapeutics
+  - tdc
+  - trillion-labs
+base_model:
+  - trillionlabs/Gravity-30B-A5B
+---
+# TxGravity-30B-A6B-Open
+## Model Summary
+**TxGravity-30B-A6B-Open** is a therapeutics-focused, task-specialized model built on the **Gravity-30B-A5B** base. It predicts a broad range of therapeutic properties — small-molecule ADMET, toxicity, drug–target interaction, protein–protein and peptide–MHC interaction, and more — following the instruction format of the **Therapeutic Data Commons (TDC)** benchmark, in the same spirit as Google's TxGemma.
+It is trained on TDC therapeutic instruction data (single-turn instruction → answer) covering 57 tasks. Answers are formatted as `(A)`/`(B)` for binary classification or a normalized `000`–`1000` bin for regression.
+| Property | Value |
+|---|---|
+| Total Parameters | 29.56B |
+| Active Parameters | ~6B |
+| Architecture | GravityMoE (DeepSeek-V3-compatible) |
+| Layers | 52 |
+| Routed / Shared Experts | 64 (top-8) / 1 |
+| Context Length | 8,192 tokens |
+| Precision | bf16 |
+| Base model | Gravity-30B-A5B |
+> ⚠️ This is a task-specialized property predictor, not a general instruction-tuned or safety-aligned assistant. Its outputs are intended for the TDC-style therapeutic prediction prompts it was trained on. Predictions may be inaccurate, biased, or incomplete and must be independently verified before any experimental, clinical, or decision-making use.
+## Performance
+Evaluated on the TDC therapeutic benchmark against a reproduced **TxGemma-27B** baseline across 60 comparable tasks:
+**16 wins · 26 ties · 18 losses**
+Selected results (TxGravity vs TxGemma-27B):
+| Task | Metric | TxGravity | TxGemma-27B |
+|---|---|---|---|
+| bindingdb_ki | PCC ↑ | **0.750** | −0.112 |
+| bindingdb_ic50 | Spearman ↑ | **0.766** | 0.643 |
+| protein_sabdab | MAE ↓ | **0.969** | 2.332 |
+| ld50_zhu | MAE ↓ | **0.651** | 0.776 |
+| drugcomb_css | MAE ↓ | **7.838** | 9.418 |
+| bioavailability_ma | AUROC ↑ | **0.749** | 0.697 |
+| bbb_martins | AUROC ↑ | **0.919** | 0.897 |
+| ames | AUROC ↑ | **0.841** | 0.830 |
+| davis | MSE ↓ | 0.760 | **0.643** |
+| kiba | MSE ↓ | 0.637 | **0.460** |
+Full per-task results (66 tasks, metric, N, margin, verdict) are in [`summary.csv`](./summary.csv).
+## Usage
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+tok = AutoTokenizer.from_pretrained("trillionlabs/TxGravity-30B-A6B-Open")
+model = AutoModelForCausalLM.from_pretrained(
+    "trillionlabs/TxGravity-30B-A6B-Open", dtype="bfloat16", device_map="auto")
+messages = [{"role": "user", "content": "<TDC-formatted instruction here>"}]
+ids = tok.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
+out = model.generate(ids, max_new_tokens=16)
+print(tok.decode(out[0][ids.shape[1]:], skip_special_tokens=True))
+```
+The model expects TDC-style prompts and answers in TDC format: `(A)`/`(B)` for classification, or a `000`–`1000` normalized bin for regression.
+## License
+Apache 2.0